Large retailers are under constant pressure to keep up with new and ever-changing customer demands. At the same time, they’re also grappling with sometimes volatile global supply chains and massive amounts of data — everything from sales and inventory data to pricing and customer insights. It’s a significant undertaking.
As more organizations continue their digital transformation process and look to migrate their data to new systems, this presents a massive opportunity to modernize their businesses, get rid of outdated systems, and even streamline and enhance the customer experience.
Such digital transformation projects can ease the burden of maintaining a single, correct view of customers, the supply chain, materials data and overall operations. But getting it right is essential — and many companies are currently getting it wrong. Taking a data-first approach is core to success.
Examining Retail Data Challenges
Adopting new technology and platforms to optimize and scale any business is a huge undertaking, especially when you’re also managing global supply chains, inventory and huge volumes of consumer data.
Retailers must manage new products, seasonal changes and segmentation of product lines; it’s all about getting the right products in the right stores at the right time while also managing shrink.
Consequently, retailers tend to have a lot of master data. They’re handling their supply chains with their vendors, and they need to figure out how to get the right inventory with the least amount of inventory on their books — all while supporting their stores and avoiding stock-outs. It’s a heavily data-driven enterprise with a lot of volume, turnover and change. This inherently creates complexity and challenges.
The Data Migration Process
Migrating data can’t be undertaken lightly. It requires bringing together data from a wide variety of sources — everything from supplier and customer transaction histories in ERP systems and point-of-sale systems. All of this information is valuable because it can be used to make informed decisions about inventory management, marketing, merchandising, sourcing and more.
Therefore, it’s key to ensure that all of the information from the right places makes the move, but it’s also important to ensure outdated or unnecessary information isn’t being ported over in the process.
That’s why evaluating relevance and cleaning data, including de-duplication efforts, is a must-do to prepare for a migration. Retailers want to ensure that they’re only migrating the data that they truly need, which is where relevance comes in. In many cases, if they’re looking to go from ERP Central Component (ECC) or other systems for retail, it’s important to thoroughly examine the data first.
Retailers need to ask themselves:
- What data do I really use?
- Have I sold or purchased the product associated with this data set in the last two years?
- Have I created the data in the last six months?
These are the types of criteria retailers want to look at to determine what data is truly active and thereby figure out what data truly needs to be included in the migration. Business relevance is key to ensuring that the retail business spends its time cleansing and governing the data that’s actually used. Efforts on data that’s not relevant is time wasted, and they want to ensure that their efforts add value.
A Data-First Approach
Retailers must be able to trust their inventory and be certain that their vendors are going to ship their product and deliver it to them effectively. If the data is wrong, then purchase orders don’t flow out and everything downstream gets messed up.
Most retailers today understand that data is the critical path, which means prioritizing data from day one is non-negotiable.
This is why a data-first mindset and approach can make a huge difference. While retail tends to be a more data-savvy industry, it’s typical for many companies to only think of data after the fact in their transformation projects. That’s to their detriment. Not dealing with the data from the outset often results in project delays, cost overruns, unreliable analytics or even outright data migration failures. By 2024, according to a McKinsey & Company report, companies around the world will waste $100 billion on failed data migrations. Digital transformation always requires data transformation.
With a data-first strategy, starting data work before or alongside a project’s global design phase is a crucial factor. And for companies that want to use generative artificial intelligence to drive value, working with data early on and concentrating on the importance of high-quality data will be paramount. Irrelevant guidance, result bias and inaccurate recommendations — all of which can hurt a brand’s reputation — can result from having poor-quality data.
Getting Data Migration Right
Data migrations present retailers with a huge opportunity to gain a better view of all the various information they must attend to. The right data can lead to the discovery of new markets, customer preferences, areas that could be more efficient, and more. But getting migrations right is key; otherwise, a poorly done data migration can create more problems than it’s worth. Getting it right requires a data-first approach. There’s never been a company that lamented starting its data quality initiative too soon. Yes, the retail industry has its data challenges, but a systematic data-first approach can ultimately make for a more successful data migration.
Today, data transformation is intrinsically linked to business transformation. It’s the foundation for delivering successful outcomes and benefits.
Jason Thompson is the senior vice president, solution architects at Syniti, the leader in enterprise data management.
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